We propose a planning algorithm to automatically generate a robust behavior plan(RBP)with which mobile robots can achive their task goal from any initial states under dynamically changing environments. For this, task description space(TDS)is formulated, where a redundant task configuration space and simulation model of physical space are employed. Successful task episodes are collected, where A algorithm is employed. Interesting TDS state vectors are extracted, where occurrence frequency is used. Clusters of TDS state vectors are found by using state transition tuples and features of state transition tuples. From these operations, characteristics of successfully performed tasks by a simulator are abstracted and generalized. Then, a robust behavior plan is constructed as an ordered tree structure, where nodes of the tree are represented by attentive TDS state vector of each cluster. The validity of our method is tested by real robot's experimentation for a box-pushing-into-a-goal task.